Driving surface protrusion pattern detection for autonomous vehicles

ABSTRACT

A component of an Autonomous Vehicle (AV) system, the component having at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to decode data encoded in a signal, wherein the data identifies a pattern of protrusions embedded in a driving surface, the signal being received from at least one vehicle sensor resulting from a vehicle driving over the pattern of protrusions in the driving surface.

TECHNICAL FIELD

Aspects described herein generally relate to autonomous vehicles (AV)and, more particularly, to techniques implementing vehicle sensor inputdata to identify a pattern of protrusions embedded in a driving surfaceof autonomous vehicles.

BACKGROUND

A location system of an AV needs to be reliable and precise. AlthoughGlobal Positioning Systems (GPS) can be used to sense AV location, GPSnot only has a precision error of around five meters, environmentalfactors can have a negative impact. Accuracy may be improved bycombining GPS with other sensing techniques, such as Assisted GPS (AGPS)or Assisted GPS (AGPS). AGPS mostly improves Time-To-First-Fix (TTFF)rather than location accuracy. And while Multi-Frequency GPS improveslocation precision, its relatively higher cost has prevented itswidespread adoption. AV location accuracy remains a critical problem tobe addressed.

BRIEF DESCRIPTION OF THE DRAWINGS

In the figures, like reference characters generally refer to the sameparts throughout the different views. The drawings are not necessarilyto scale, emphasis instead generally being placed upon illustrating theprinciples of the disclosure. In the following description, variousaspects of the disclosure are described with reference to the followingfigures in which:

FIG. 1 illustrates an exemplary autonomous vehicle in accordance withvarious aspects of the present disclosure;

FIG. 2 illustrates various exemplary electronic components of a controlsystem of the autonomous vehicle of FIG. 1 in accordance with variousaspects of the present disclosure;

FIG. 3 illustrates an exemplary pattern of protrusions embedded in adriving surface in accordance with various aspects of the presentdisclosure;

FIG. 4 illustrates an exemplary encoding technique to generate a datasignal from a pattern of protrusions in accordance with various aspectsof the present disclosure; and

FIG. 5 illustrates an exemplary pressure-reactive protrusion embedded ina driving surface in accordance with various aspects of the presentdisclosure.

DESCRIPTION OF THE ASPECTS

The present disclosure is directed to detecting vehicle location basedon mechanical feedback resulting from a vehicle driving over protrusionsembedded in a driving surface. Also, vehicle location accuracy may beimproved upon by supplementing location information of an existinglocation system with the vehicle location information described herein.

Various aspects are described throughout the disclosure with referenceto autonomous vehicles by way of example and not limitation. Forinstance, although the aspects described herein may advantageously beused as part of an autonomous vehicle architecture, the aspects may beimplemented as part of any suitable type of fully autonomous vehicle,semi-autonomous vehicle, or non-autonomous vehicle.

FIG. 1 illustrates an exemplary autonomous vehicle (AV) 100, and FIG. 2illustrates various exemplary electronic components of a control systemof the AV of FIG. 1 in accordance with various aspects of the presentdisclosure. The vehicle 100 is shown on a vehicle surface havingprotrusions 302 (402 and/or 502) embedded therein, as will be describedfurther below.

The vehicle 100 and the control system 200 are exemplary in nature, andmay thus be simplified for explanatory purposes. Locations of elementsand relational distances (as discussed above, the figures are not toscale) and are provided by way of example and not limitation. Thecontrol system 200 may include various components depending on therequirements of a particular implementation.

The control system 200 may include one or more processors 102, one ormore image acquisition devices 104 such as, e.g., one or more cameras,one or more position sensors such as a Global Navigation SatelliteSystem (GNSS), e.g., a Global Positioning System (GPS) (not shown), oneor more vibration sensors 106, one or more memories 202, one or more mapdatabases 204, one or more user interfaces 206 (such as, e.g., adisplay, a touch screen, a microphone, a loudspeaker, one or morebuttons and/or switches, and the like), one or more wirelesstransceivers 208, 210, 212, and part of the cloud 114.

The wireless transceivers 208, 210, 212 may be configured according todifferent desired radio communication protocols or standards. By way ofexample, a wireless transceiver (e.g., a first wireless transceiver 208)may be configured in accordance with a Short Range mobile radiocommunication standard such as e.g. Bluetooth, Zigbee, and the like. Asanother example, a wireless transceiver (e.g., a second wirelesstransceiver 210) may be configured in accordance with a Medium or WideRange mobile radio communication standard such as e.g. a 3G (e.g.Universal Mobile Telecommunications System—UMTS), a 4G (e.g. Long TermEvolution—LTE), or a 5G mobile radio communication standard inaccordance with corresponding 3GPP (3rd Generation Partnership Project)standards. As a further example, a wireless transceiver (e.g., a thirdwireless transceiver 212) may be configured in accordance with aWireless Local Area Network communication protocol or standard such ase.g. in accordance with IEEE 802.11 (e.g. 802.11, 802.11a, 802.11b,802.11g, 802.11n, 802.11p, 802.11-12, 802.11ac, 802.11ad, 802.11ah, andthe like). The one or more wireless transceivers 208, 210, 212 may beconfigured to transmit signals via an antenna system (not shown) via anair interface.

The one or more processors 102 may be located in the vehicle'sElectronic Control Unit (ECU) and/or any other suitable location of thevehicle 100. Alternatively or additionally, the one or more processors102 may be located in the cloud 114. The location of the one or moreprocessors 102 is not limited.

The one or more processors 102 may include an application processor 214,an image processor 216, a communication processor 218, or any othersuitable processing device. Similarly, image acquisition devices 104 mayinclude any number of image acquisition devices and components dependingon the requirements of a particular application. Image acquisitiondevices 104 may include one or more image capture devices (e.g.,cameras, charge coupling devices (CCDs), or any other type of imagesensor). The control system 200 may also include a data interfacecommunicatively connecting the one or more processors 102 to the one ormore image acquisition devices 104. For example, a first data interfacemay include any wired and/or wireless first link 220, or first links 220for transmitting image data acquired by the one or more imageacquisition devices 104 to the one or more processors 102, e.g., to theimage processor 216.

The wireless transceivers 208, 210, 212 may be coupled to the one ormore processors 102, e.g., to the communication processor 218, e.g., viaa second data interface. The second data interface may include any wiredand/or wireless second link 222 or second links 222 for transmittingradio transmitted data acquired by wireless transceivers 208, 210, 212to the one or more processors 102, e.g., to the communication processor218.

The memories 202 as well as the one or more user interfaces 206 may becoupled to each of the one or more processors 102, e.g., via a thirddata interface. The third data interface may include any wired and/orwireless third link 224 or third links 224. Furthermore, the vibrationsensor 106 may be coupled to each of the one or more processors 102,e.g., via the third data interface. The vibration sensors 106 may belocated in any suitable location within the vehicle 100.

Such transmissions may also include communications (one-way or two-way)between the vehicle 100 and one or more other (target) vehicles in anenvironment of the vehicle 100 (e.g., to facilitate coordination ofnavigation of the vehicle 100 in view of or together with other (target)vehicles in the environment of the vehicle 100), or even a broadcasttransmission to unspecified recipients in a vicinity of the transmittingvehicle 100.

One or more of the transceivers 208, 210, 212 may be configured toimplement one or more vehicle to everything (V2X) communicationprotocols, which may include vehicle to vehicle (V2V), vehicle toinfrastructure (V2I), vehicle to network (V2N), vehicle to pedestrian(V2P), vehicle to device (V2D), vehicle to grid (V2G), and any othersuitable protocols.

Each processor 214, 216, 218 of the one or more processors 102 mayinclude various types of hardware-based processing devices. By way ofexample, each processor 214, 216, 218 may include a microprocessor,pre-processors (such as an image pre-processor), graphics processors, acentral processing unit (CPU), support circuits, digital signalprocessors, integrated circuits, memory, or any other types of devicessuitable for running applications and for data processing (e.g. image,audio, etc.) and analysis. In some aspects, each processor 214, 216, 218may include any type of single or multi-core processor, mobile devicemicrocontroller, central processing unit, etc. These processor types mayeach include multiple processing units with local memory and instructionsets. Such processors may include video inputs for receiving image datafrom multiple image sensors, and may also include video outcapabilities.

Any of the processors 214, 216, 218 disclosed herein may be configuredto perform certain functions in accordance with program instructionswhich may be stored in a memory of the one or more memories 202. Inother words, a memory of the one or more memories 202 may store softwarethat, when executed by a processor (e.g., by the one or more processors102), controls the operation of the system, e.g., the control system. Amemory of the one or more memories 202 may store one or more databasesand image processing software, as well as a trained system, such as aneural network, a deep neural network, and/or a convolutional deepneural network (CNN), for example, as further discussed herein. The oneor more memories 202 may include any number of random access memories,read only memories, flash memories, disk drives, optical storage, tapestorage, removable storage, and other types of storage.

In some aspects, the control system 200 may further include componentssuch as a speed sensor 108 (e.g., a speedometer) for measuring a speedof the vehicle 100. The control system may also include one or moreaccelerometers (either single axis or multi axis) (not shown) formeasuring accelerations of the vehicle 100 along one or more axes. Thecontrol system 200 may further include additional sensors or differentsensor types such as an ultrasonic sensor, a thermal sensor, one or moreradar sensors 110, one or more LIDAR sensors 112 (which may beintegrated in the head lamps of the vehicle 100), digital compasses, andthe like. The radar sensors 110 and/or the LIDAR sensors 112 may beconfigured to provide pre-processed sensor data, such as radar targetlists or LIDAR target lists. The third data interface (e.g., one or morelinks 224) may couple the speed sensor 108, the one or more radarsensors 110, and the one or more LIDAR sensors 112 to at least one ofthe one or more processors 102.

The one or more memories 202 may store data, e.g., in a database or inany different format, that, e.g., indicate a location of knownlandmarks. The one or more processors 102 may process sensoryinformation (such as images, radar signals, depth information from LIDARor stereo processing of two or more images) of the environment of thevehicle 100 together with position information, such as a GPScoordinate, a vehicle's ego-motion, etc., to determine a currentlocation and/or orientation of the vehicle 100 relative to the knownlandmarks and refine the determination of the vehicle's location.Certain aspects of this technology may be included in a localizationtechnology such as a mapping and routing model.

The map database 204 may include any suitable type of database storing(digital) map data for the vehicle 100, e.g., for the control system200. The map database 204 may include data relating to the position, ina reference coordinate system, of various items, including roads, waterfeatures, geographic features, businesses, points of interest,restaurants, gas stations, etc. The map database 204 may store not onlythe locations of such items, but also descriptors relating to thoseitems, including, for example, names associated with any of the storedfeatures. In such aspects, a processor of the one or more processors 102may download information from the map database 204 over a wired orwireless data connection to a communication network (e.g., over acellular network and/or the Internet, etc.). In some cases, the mapdatabase 204 may store a sparse data model including polynomialrepresentations of certain road features (e.g., lane markings) or targettrajectories for the vehicle 100. The map database 204 may also includestored representations of various recognized landmarks that may beprovided to determine or update a known position of the vehicle 100 withrespect to a target trajectory. The landmark representations may includedata fields such as landmark type, landmark location, among otherpotential identifiers.

FIG. 3 illustrates an exemplary pattern of protrusions 302 embedded in adriving surface in accordance with various aspects of the presentdisclosure. More detailed examples of the protrusions 302 are describedbelow with respect to FIGS. 4 and 5 .

The protrusions 302 are shown as disc-shaped. This shape is not meant tobe limiting. Alternatively, one or more of the protrusions 302 may havea shape that is elongated, dome-like, or any other shape(s) as suitable.Also, the height(s) of the protrusions 302 is not limited.

The protrusions 302 are arranged in a pattern to provide information.This information may be location information that assists vehiclelocation efforts. The location information may be geographic, a name, afloor number, etc. The information may additionally or alternativelyinclude other information, which may be any type of information asdesired.

By way of overview, when the vehicle 100 drives over the pattern ofprotrusions 302, one or more of the vehicle's vibration sensors 106sense vibrations caused by pressure between the protrusions 302 and anyof the vehicle tires. The one or more vibration sensors 106 output asignal having data encoded thereon representing the pattern information.The one or more processors 102 are configured to receive the signal fromthe vibration sensors 106 and decode the data to identify the pattern ofprotrusions 302 representing the information.

FIG. 4 illustrates an exemplary encoding technique to generate a datasignal from a pattern of protrusions in accordance with various aspectsof the present disclosure.

The protrusions 402 in this example are passive and form a passivearray. When a vehicle tire runs over a passive protrusion 402, thepassive protrusion 402 remains in a fixed vertical position.

As discussed above with respect to FIG. 3 , the vibration sensors 106output a vibration signal, in this case represented by reference numeral404. The signal 404 comprises peaks and nulls corresponding with theprotrusions 402 and lack thereof, respectively, each separated by adistance d. The peaks and nulls are separated in time (t=d/v) dependingon a speed or velocity v of the vehicle 100, and distance dtherebetween. The signal shapes are simplified for illustration, but maybe more complex in actual implementation.

The processor 102 is configured to decode data encoded in the signal 404that identifies a pattern of protrusions 402 embedded in a drivingsurface. The signals 404 are received by the processor 102 from at leastone vehicle vibration sensor 106. The one or more processors 102 decodesthe signals 404 into a series of bits. In this example a peak is decodedinto a logical “1” and a null into a logical “0”, though the disclosureis not limited in this respect. In this example shown in the figure, thesignal 404 is decoded into 101101. The one or more processors 102 factorin a speed and/or direction of the vehicle 100 based on a speed signalreceived from the speed sensor 108.

It is possible the vehicle 100 will traverse the protrusions 402 at anangle, resulting in a series of vibrations delayed and a slightlydifferent distance between the signal peaks and nulls. The one or moreprocessors 102 may be configured to compensate for this variation,possibly based on a direction the vehicle is traveling. The directioninformation may be known from GPS.

The processor 102 may decode data encoded in a plurality of signalsreceived from a plurality of vehicle vibration sensors 106. Also, thesensed vibrations may be complex due to echoing. For example, a sensor106 located in a cabin of the vehicle 100 may sense an initialvibration, and then reflections within the cabin may result in thevibration sensor 106 sensing a similar signal but delayed in time. Andthen when the rear tires subsequently traverse the same protrusions 402as a front tire, the vibration sensors 106 will sense a different set ofvibrations. The one or more processors 102 are configured to processthese multiple signals into actual data.

The pattern of protrusions 402 may be designed to cause error detectiondata to be encoded in the signal 404. The error detection may be ForwardError Correction (FEC), or alternatively, any detection and/orcorrection method as is suitable. Additionally or alternatively, thepattern of protrusions 302 may be designed to cause redundant data to beencoded in the signal 404. A redundant pattern of protrusions 402 may bepositioned to be driven over by a same vehicle tire, or alternatively,different vehicle tires. The processor 102 may be configured to performerror detection, and possibly also correction, of the data. Data maythen still be accurately recognized even when the environment is noisyor the protrusions 402 have been damaged.

The processor 102 may provide the decoded data to another system tosupplement data of the other system. The other system may be one or moreof a Global Positioning System (GPS), a camera system, a radar system,and a Light Detection and Ranging (LIDAR) system. If the data encoded inthe signal 404 is location data, the processor 102 may provide thelocation data to another location system to improve location detectionaccuracy.

FIG. 5 illustrates an exemplary pressure-reactive protrusion 502embedded in a driving surface in accordance with various aspects of thepresent disclosure.

The pressure-reactive protrusion 502 may replace one or more of thepassive protrusions 402 of FIG. 4 to form an active array. Rather thanbeing in a fixed vertical position, the pressure-reactive protrusion 502is configured to react to vehicle pressure. The pressure-reactiveprotrusion 502 may be similar to an automatic center punch used to breakvehicle windows in emergency situations. A spring mechanism storesenergy when a vehicle tire presses the protrusion 502. Past a certainthreshold, the spring is released as an impulse that drives a pistoninto the vehicle tire. The disclosure is not limited to this particularmechanical design.

The resulting vibration signal 504 has, from a single press of one ofthe pressure-reactive protrusion 502, a first signal peak 504.1 thatidentifies the vehicle pressure on the pressure-reactive protrusion 502,and a second signal peak 504.2 that identifies the correspondingreaction of the pressure-reactive protrusion 502. Of course the signalcould be more complex in actual implementation. The one or moreprocessors 102 are configured to decode the data encoded in thevibration signal into corresponding multiple bits identifying thevehicle pressure on the pressure-reactive protrusion and thecorresponding reaction. The pressure-reactive protrusion 502 is saferand more robust against hacking than the passive protrusion 402 due to amulti-bit signature that is more difficult to replace or reproduce.

Driving surface protrusion pattern detection as disclosed here resultsin improved vehicle location accuracy to within the centimeter range,even during a power shortage. Error correction and/or redundancy of thevibration pattern allows for decoding even when there is damage toprotrusion array, such as partial removal of the embedded protrusions.Further, pressure-reactive protrusions 502 advantageously preventmalicious attacks.

EXAMPLES

The following examples pertain to further aspects.

Example 1. A component of an Autonomous Vehicle (AV) system, thecomponent comprising: at least one processor; and a non-transitorycomputer-readable storage medium including instructions that, whenexecuted by the at least one processor, cause the at least one processorto decode data encoded in a signal, wherein the data identifies apattern of protrusions embedded in a driving surface, the signal beingreceived from at least one vehicle sensor resulting from a vehicledriving over the pattern of protrusions in the driving surface.

Example 2. The component of example 1, wherein the pattern ofprotrusions comprises a passive array having fixed protrusions.

Example 3. The component of example 1, wherein the pattern ofprotrusions comprises an active array having pressure-reactiveprotrusions.

Example 4. The component of example 3, wherein the pressure-reactiveprotrusions are configured to react to vehicle pressure such that thepressure signal has, from a single press of one of the pressure-reactiveprotrusions, a first signal peak that identifies the vehicle pressure onthe pressure-reactive protrusion, and a second signal peak thatidentifies the corresponding reaction, and the instructions furthercause the at least one processor to decode the data encoded in thevibration signal, wherein the data identifies the vehicle pressure onthe pressure-reactive protrusion and the corresponding reaction.

Example 5. The component of example 1, wherein the instructions furthercause the at least one processor to determine physical separations ofthe protrusions based on a speed of the vehicle.

Example 6. The component of example 1, wherein the pattern ofprotrusions cause error detection data to be encoded in the signal, andthe instructions further cause the at least one processor to performerror detection of the decoded data.

Example 7. The component of example 1, wherein the pattern ofprotrusions cause error detection data to be encoded in the signal, andthe instructions further cause the at least one processor to performForward Error Correction (FEC) of the decoded data.

Example 8. The component of example 1, wherein the pattern ofprotrusions causes redundant data to be encoded in the signal, and theinstructions further cause the at least one processor to use theredundant data to perform error detection of the decoded data.

Example 9. The component of example 1, wherein the data encoded in thesignal is location data.

Example 10. The component of example 1, wherein the instructions furthercause the at least one processor to provide the decoded data to anothersystem to supplement data of the other system with the decoded data.

Example 11. The component of example 10, wherein the system is one ormore of a Global Positioning System (GPS), a camera system, a radarsystem, and a Light Detection and Ranging (LIDAR) system.

Example 12. The component of example 1, wherein the data encoded in thesignal is location data, and the instructions further cause the at leastone processor to provide the location data to a location system tosupplement location data used by the location system with the decodedlocation data.

Example 13. The component of example 1, wherein the signal comprisessignal peaks and nulls corresponding with protrusions and lack thereof,separated in time depending on a speed or direction of the vehicle, andthe instructions further cause the at least one processor to decode thedata encoded in the signal factoring in the speed or direction of thevehicle based on a speed signal received from a speed sensor.

Example 14. The component of example 1, wherein the instructions furthercause the at least one processor to decode data encoded in a pluralityof signals received from a plurality of vehicle sensors.

Example 15. The component of example 1, wherein the component is locatedin an Electronic Control Unit (ECU) of the AV.

Example 16. The component of example 1, wherein the component is locatedin the cloud.

Example 17. An Autonomous Vehicle (AV), comprising: at least one vehiclesensor; and a component comprising: at least one processor; and anon-transitory computer-readable storage medium including instructionsthat, when executed by the at least one processor, cause the at leastone processor to decode data encoded in a signal, wherein the dataidentifies a pattern of protrusions embedded in a driving surface, thesignal being received from the at least one vehicle sensor resultingfrom the AV driving over the pattern of protrusions in the drivingsurface.

Example 18. The AV of example 17, wherein the pattern of protrusionscomprises a passive array having fixed protrusions.

Example 19. The AV of example 17, wherein the pattern of protrusionscomprises an active array having pressure-reactive protrusions.

Example 20. The AV of example 17, wherein the data encoded in the signalis location data, and the instructions further cause the at least oneprocessor to provide the location data to a location system tosupplement location data used by the location system with the decodedlocation data.

Example 21. A non-transitory computer-readable storage medium includinginstructions that, when executed by at least one processor of acomponent of an Autonomous Vehicle (AV) system, cause the at least oneprocessor to decode data encoded in a signal, wherein the dataidentifies a pattern of protrusions embedded in a driving surface, thesignal being received from at least one vehicle sensor resulting from avehicle driving over the pattern of protrusions in the driving surface.

Example 22. The non-transitory computer-readable storage medium ofexample 21, wherein the pattern of protrusions cause error detectiondata to be encoded in the signal, and the instructions further cause theat least one processor to perform error detection of the decoded data.

Example 23. The non-transitory computer-readable storage medium ofexample 21, wherein the pattern of protrusions cause error detectiondata to be encoded in the signal, and the instructions further cause theat least one processor to perform Forward Error Correction (FEC) of thedecoded data.

Example 24. The non-transitory computer-readable storage medium ofexample 21, wherein the pattern of protrusions causes redundant data tobe encoded in the signal, and the instructions further cause the atleast one processor to use the redundant data to perform error detectionof the decoded data.

Example 25. The component of example 24, wherein the instructionsfurther cause the at least one processor to determine physicalseparations of the protrusions based on a speed of the vehicle.

Example 26 is an apparatus as shown and described.

Example 27 is a method as shown and described.

CONCLUSION

The aforementioned description of the specific aspects will so fullyreveal the general nature of the disclosure that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific aspects, without undueexperimentation, and without departing from the general concept of thepresent disclosure. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed aspects, based on the teaching and guidance presented herein.It is to be understood that the phraseology or terminology herein is forthe purpose of description and not of limitation, such that theterminology or phraseology of the present specification is to beinterpreted by the skilled artisan in light of the teachings andguidance.

References in the specification to “one aspect,” “an aspect,” “anexemplary aspect,” etc., indicate that the aspect described may includea particular feature, structure, or characteristic, but every aspect maynot necessarily include the particular feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same aspect. Further, when a particular feature, structure, orcharacteristic is described in connection with an aspect, it issubmitted that it is within the knowledge of one skilled in the art toaffect such feature, structure, or characteristic in connection withother aspects whether or not explicitly described.

The exemplary aspects described herein are provided for illustrativepurposes, and are not limiting. Other exemplary aspects are possible,and modifications may be made to the exemplary aspects. Therefore, thespecification is not meant to limit the disclosure. Rather, the scope ofthe disclosure is defined only in accordance with the following claimsand their equivalents.

Aspects may be implemented in hardware (e.g., circuits), firmware,software, or any combination thereof. Aspects may also be implemented asinstructions stored on a machine-readable medium, which may be read andexecuted by one or more processors. A machine-readable medium mayinclude any mechanism for storing or transmitting information in a formreadable by a machine (e.g., a computing device). For example, amachine-readable medium may include read only memory (ROM); randomaccess memory (RAM); magnetic disk storage media; optical storage media;flash memory devices; electrical, optical, acoustical or other forms ofpropagated signals (e.g., carrier waves, infrared signals, digitalsignals, etc.), and others. Further, firmware, software, routines,instructions may be described herein as performing certain actions.However, it should be appreciated that such descriptions are merely forconvenience and that such actions in fact results from computingdevices, processors, controllers, or other devices executing thefirmware, software, routines, instructions, etc. Further, any of theimplementation variations may be carried out by a general purposecomputer.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration”. Any embodiment or design described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs.

Throughout the drawings, it should be noted that like reference numbersare used to depict the same or similar elements, features, andstructures, unless otherwise noted.

The terms “at least one” and “one or more” may be understood to includea numerical quantity greater than or equal to one (e.g., one, two,three, four, [ . . . ], etc.). The term “a plurality” may be understoodto include a numerical quantity greater than or equal to two (e.g., two,three, four, five, [ . . . ], etc.).

The words “plural” and “multiple” in the description and in the claimsexpressly refer to a quantity greater than one. Accordingly, any phrasesexplicitly invoking the aforementioned words (e.g., “plural [elements]”,“multiple [elements]”) referring to a quantity of elements expresslyrefers to more than one of the said elements. The terms “group (of)”,“set (of)”, “collection (of)”, “series (of)”, “sequence (of)”, “grouping(of)”, etc., and the like in the description and in the claims, if any,refer to a quantity equal to or greater than one, i.e., one or more. Theterms “proper subset”, “reduced subset”, and “lesser subset” refer to asubset of a set that is not equal to the set, illustratively, referringto a subset of a set that contains less elements than the set.

The phrase “at least one of” with regard to a group of elements may beused herein to mean at least one element from the group consisting ofthe elements. For example, the phrase “at least one of” with regard to agroup of elements may be used herein to mean a selection of: one of thelisted elements, a plurality of one of the listed elements, a pluralityof individual listed elements, or a plurality of a multiple ofindividual listed elements.

The term “data” as used herein may be understood to include informationin any suitable analog or digital form, e.g., provided as a file, aportion of a file, a set of files, a signal or stream, a portion of asignal or stream, a set of signals or streams, and the like. Further,the term “data” may also be used to mean a reference to information,e.g., in form of a pointer. The term “data”, however, is not limited tothe aforementioned examples and may take various forms and represent anyinformation as understood in the art.

The terms “processor” or “controller” as, for example, used herein maybe understood as any kind of technological entity that allows handlingof data. The data may be handled according to one or more specificfunctions executed by the processor or controller. Further, a processoror controller as used herein may be understood as any kind of circuit,e.g., any kind of analog or digital circuit. A processor or a controllermay thus be or include an analog circuit, digital circuit, mixed-signalcircuit, logic circuit, processor, microprocessor, Central ProcessingUnit (CPU), Graphics Processing Unit (GPU), Digital Signal Processor(DSP), Field Programmable Gate Array (FPGA), integrated circuit,Application Specific Integrated Circuit (ASIC), etc., or any combinationthereof. Any other kind of implementation of the respective functions,which will be described below in further detail, may also be understoodas a processor, controller, or logic circuit. It is understood that anytwo (or more) of the processors, controllers, or logic circuits detailedherein may be realized as a single entity with equivalent functionalityor the like, and conversely that any single processor, controller, orlogic circuit detailed herein may be realized as two (or more) separateentities with equivalent functionality or the like.

As used herein, “memory” is understood as a computer-readable medium inwhich data or information can be stored for retrieval. References to“memory” included herein may thus be understood as referring to volatileor non-volatile memory, including random access memory (RAM), read-onlymemory (ROM), flash memory, solid-state storage, magnetic tape, harddisk drive, optical drive, among others, or any combination thereof.Registers, shift registers, processor registers, data buffers, amongothers, are also embraced herein by the term memory. The term “software”refers to any type of executable instruction, including firmware.

In one or more of the exemplary aspects described herein, processingcircuitry can include memory that stores data and/or instructions. Thememory can be any well-known volatile and/or non-volatile memory,including, for example, read-only memory (ROM), random access memory(RAM), flash memory, a magnetic storage media, an optical disc, erasableprogrammable read only memory (EPROM), and programmable read only memory(PROM). The memory can be non-removable, removable, or a combination ofboth.

Unless explicitly specified, the term “transmit” encompasses both direct(point-to-point) and indirect transmission (via one or more intermediarypoints). Similarly, the term “receive” encompasses both direct andindirect reception. Furthermore, the terms “transmit,” “receive,”“communicate,” and other similar terms encompass both physicaltransmission (e.g., the transmission of radio signals) and logicaltransmission (e.g., the transmission of digital data over a logicalsoftware-level connection). For example, a processor or controller maytransmit or receive data over a software-level connection with anotherprocessor or controller in the form of radio signals, where the physicaltransmission and reception is handled by radio-layer components such asRF transceivers and antennas, and the logical transmission and receptionover the software-level connection is performed by the processors orcontrollers. The term “communicate” encompasses one or both oftransmitting and receiving, i.e., unidirectional or bidirectionalcommunication in one or both of the incoming and outgoing directions.The term “calculate” encompasses both ‘direct’ calculations via amathematical expression/formula/relationship and ‘indirect’ calculationsvia lookup or hash tables and other array indexing or searchingoperations.

A “vehicle” may be understood to include any type of driven object. Byway of example, a vehicle may be a driven object with a combustionengine, a reaction engine, an electrically driven object, a hybriddriven object, or a combination thereof. A vehicle may be or may includean automobile, a bus, a mini bus, a van, a truck, a mobile home, avehicle trailer, a motorcycle, a bicycle, a tricycle, a trainlocomotive, a train wagon, a moving robot, a personal transporter, andthe like.

A “ground vehicle” may be understood to include any type of vehicle, asdescribed above, which is driven on the ground, e.g., on a street, on aroad, on a track, on one or more rails, off-road, etc.

The term “autonomous vehicle” may describe a vehicle that implements allor substantially all navigational changes, at least during some(significant) part (spatial or temporal, e.g., in certain areas, or whenambient conditions are fair, or on highways, or above or below a certainspeed) of some drives. Sometimes an “autonomous vehicle” isdistinguished from a “partially autonomous vehicle” or a“semi-autonomous vehicle” to indicate that the vehicle is capable ofimplementing some (but not all) navigational changes, possibly atcertain times, under certain conditions, or in certain areas. Anavigational change may describe or include a change in one or more ofsteering, braking, or acceleration/deceleration of the vehicle. Avehicle may be described as autonomous even in case the vehicle is notfully automatic (for example, fully operational with driver or withoutdriver input). Autonomous vehicles may include those vehicles that canoperate under driver control during certain time periods and withoutdriver control during other time periods. Autonomous vehicles may alsoinclude vehicles that control only some aspects of vehicle navigation,such as steering (e.g., to maintain a vehicle course between vehiclelane constraints) or some steering operations under certaincircumstances (but not under all circumstances), but may leave otheraspects of vehicle navigation to the driver (e.g., braking or brakingunder certain circumstances). Autonomous vehicles may also includevehicles that share the control of one or more aspects of vehiclenavigation under certain circumstances (e.g., hands-on, such asresponsive to a driver input) and vehicles that control one or moreaspects of vehicle navigation under certain circumstances (e.g.,hands-off, such as independent of driver input). Autonomous vehicles mayalso include vehicles that control one or more aspects of vehiclenavigation under certain circumstances, such as under certainenvironmental conditions (e.g., spatial areas, roadway conditions). Insome aspects, autonomous vehicles may handle some or all aspects ofbraking, speed control, velocity control, and/or steering of thevehicle. An autonomous vehicle may include those vehicles that canoperate without a driver. The level of autonomy of a vehicle may bedescribed or determined by the Society of Automotive Engineers (SAE)level of the vehicle (e.g., as defined by the SAE, for example in SAEJ3016 2018: Taxonomy and definitions for terms related to drivingautomation systems for on road motor vehicles) or by other relevantprofessional organizations. The SAE level may have a value ranging froma minimum level, e.g. level 0 (illustratively, substantially no drivingautomation), to a maximum level, e.g. level 5 (illustratively, fulldriving automation).

While the foregoing has been described in conjunction with exemplaryaspect, it is understood that the term “exemplary” is merely meant as anexample, rather than the best or optimal. Accordingly, the disclosure isintended to cover alternatives, modifications and equivalents, which maybe included within the scope of the disclosure.

Although specific aspects have been illustrated and described herein, itwill be appreciated by those of ordinary skill in the art that a varietyof alternate and/or equivalent implementations may be substituted forthe specific aspects shown and described without departing from thescope of the present application. This application is intended to coverany adaptations or variations of the specific aspects discussed herein.

The invention claimed is:
 1. A component of an Autonomous Vehicle (AV)system, the component comprising: at least one processor; and anon-transitory computer-readable storage medium including instructionsthat, when executed by the at least one processor, cause the at leastone processor: to decode data encoded in a signal, wherein the dataidentifies a pattern of protrusions embedded in a driving surface, thesignal being received from at least one vehicle sensor resulting from avehicle driving over the pattern of protrusions in the driving surface,and to transmit the decoded data to another system to supplement data ofthe other system with the decoded data for controlling the AV, whereinthe pattern of protrusions comprises an active array havingpressure-reactive protrusions, and the pressure-reactive protrusions areconfigured to react to vehicle pressure such that the signal, which is apressure signal, has, from a single press of one of thepressure-reactive protrusions, a first signal peak that identifies thevehicle pressure on the respective pressure-reactive protrusion, and asecond signal peak that identifies a corresponding reaction by therespective pressure-reactive protrusion, wherein the first and secondpeaks are both positive peaks or both negative peaks, and theinstructions further cause the at least one processor to decode the dataencoded in the pressure signal, wherein the data identifies the vehiclepressure on the pressure-reactive protrusions and the correspondingreactions.
 2. The component of claim 1, wherein the pattern ofprotrusions additionally comprises a passive array having fixedprotrusions.
 3. The component of claim 1, wherein the instructionsfurther cause the at least one processor to determine physicalseparations of the protrusions based on a speed of the vehicle.
 4. Thecomponent of claim 1, wherein the pattern of protrusions cause errordetection data to be encoded in the signal, and the instructions furthercause the at least one processor to perform error detection of thedecoded data.
 5. The component of claim 1, wherein the pattern ofprotrusions cause error detection data to be encoded in the signal, andthe instructions further cause the at least one processor to performForward Error Correction (FEC) of the decoded data.
 6. The component ofclaim 1, wherein the pattern of protrusions causes redundant data to beencoded in the signal, and the instructions further cause the at leastone processor to use the redundant data to perform error detection ofthe decoded data.
 7. The component of claim 1, wherein the data encodedin the signal is location data.
 8. The component of claim 1, wherein theanother system is one or more of a Global Positioning System (GPS), acamera system, a radar system, and a Light Detection and Ranging (LIDAR)system.
 9. The component of claim 1, wherein the data encoded in thesignal is location data, and the instructions further cause the at leastone processor to provide the location data to a location system tosupplement location data used by the location system with the decodedlocation data.
 10. The component of claim 1, wherein the signalcomprises signal peaks and nulls corresponding with protrusions and lackthereof, separated in time depending on a speed or direction of thevehicle, and the instructions further cause the at least one processorto decode the data encoded in the signal factoring in the speed ordirection of the vehicle based on a speed signal received from a speedsensor.
 11. The component of claim 1, wherein the instructions furthercause the at least one processor to decode data encoded in a pluralityof signals received from the at least one vehicle sensor.
 12. Thecomponent of claim 1, wherein the component is located in an ElectronicControl Unit (ECU) of the AV.
 13. The component of claim 1, wherein thecomponent is located in the cloud.
 14. An Autonomous Vehicle (AV),comprising: at least one vehicle sensor; and a component comprising: atleast one processor; and a non-transitory computer-readable storagemedium including instructions that, when executed by the at least oneprocessor, cause the at least one processor: to decode data encoded in asignal, wherein the data identifies a pattern of protrusions embedded ina driving surface, the signal being received from the at least onevehicle sensor resulting from the AV driving over the pattern ofprotrusions in the driving surface, wherein the data encoded in thesignal is location data, and to pry transmit the location data to alocation system to supplement location data used by the location systemwith the decoded location data for controlling the AV, wherein thepattern of protrusions comprises an active array havingpressure-reactive protrusions, and the pressure-reactive protrusions areconfigured to react to vehicle pressure such that the signal, which is apressure signal, has, from a single press of one of thepressure-reactive protrusions, a first signal peak that identifies thevehicle pressure on the respective pressure-reactive protrusion, and asecond signal peak that identifies a corresponding reaction by therespective pressure-reactive protrusion, wherein the first and secondpeaks are both positive peaks or both negative peaks, and theinstructions further cause the at least one processor to decode the dataencoded in the pressure signal, wherein the data identifies the vehiclepressure on the pressure-reactive protrusions and the correspondingreactions.
 15. The AV of claim 14, wherein the pattern of protrusionscomprises a passive array having fixed protrusions.
 16. A non-transitorycomputer-readable storage medium including instructions that, whenexecuted by at least one processor of a component of an AutonomousVehicle (AV) system, cause the at least one processor to decode dataencoded in a signal, wherein the data identifies a pattern ofprotrusions embedded in a driving surface, the signal being receivedfrom at least one vehicle sensor resulting from a vehicle driving overthe pattern of protrusions in the driving surface, and to transmit thedecoded data to another system to supplement data of the other systemwith the decoded data for controlling the AV, wherein the pattern ofprotrusions comprises an active array having pressure-reactiveprotrusions, and the pressure-reactive protrusions are configured toreact to vehicle pressure such that the signal, which is a pressuresignal, has, from a single press of one of the pressure-reactiveprotrusions, a first signal peak that identifies the vehicle pressure onthe respective pressure-reactive protrusion, and a second signal peakthat identifies a corresponding reaction by the respectivepressure-reactive protrusion, wherein the first and second peaks areboth positive peaks or both negative peaks, and the instructions furthercause the at least one processor to decode the data encoded in thepressure signal, wherein the data identifies the vehicle pressure on thepressure-reactive protrusions and the corresponding reactions.
 17. Thenon-transitory computer-readable storage medium of claim 16, wherein thepattern of protrusions cause error detection data to be encoded in thesignal, and the instructions further cause the at least one processor toperform error detection of the decoded data.
 18. The non-transitorycomputer-readable storage medium of claim 16, wherein the pattern ofprotrusions cause error detection data to be encoded in the signal, andthe instructions further cause the at least one processor to performForward Error Correction (FEC) of the decoded data.
 19. Thenon-transitory computer-readable storage medium of claim 16, wherein thepattern of protrusions causes redundant data to be encoded in thesignal, and the instructions further cause the at least one processor touse the redundant data to perform error detection of the decoded data.20. The component of claim 19, wherein the instructions further causethe at least one processor to determine physical separations of theprotrusions based on a speed of the vehicle.